ML with R - KNN Project

K Nearest Neighbour Project

Since KNN is such a simple algorithm, we will just use this “Project” as a simple exercise to test your understanding of the implementation of KNN.

Iris Dataset

We’ll use the famous iris data set for this project. It’s a small data set with flower features that can be used to attempt to predict the species of an iris flower.

if(!require(ggpubr)){
  install.packages("ggpubr")
  library(ggpubr)
}

if(!require(ISLR2)){
  install.packages("ISLR2")
  library(ISLR2)
}

if(!require(ggplot2)){
  install.packages("ggplot2")
  library(ggplot2)
}

if(!require(ggdark)){
  install.packages("ggdark")
  library(ggdark)
}

if(!require(caTools)){
  install.packages("caTools")
  library(caTools)
}

if(!require(class)){
  install.packages("class")
  library(class)
}

if(!require(plotly)){
  install.packages("plotly")
  library(plotly)
}

df <- iris
head(df)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

str(df)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

summary(df)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

standard.feature <- scale( df[1:4])

var(standard.feature[,1])

## [1] 1

final.data <- cbind(standard.feature,df[5])
head(final.data)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1   -0.8976739  1.01560199    -1.335752   -1.311052  setosa
## 2   -1.1392005 -0.13153881    -1.335752   -1.311052  setosa
## 3   -1.3807271  0.32731751    -1.392399   -1.311052  setosa
## 4   -1.5014904  0.09788935    -1.279104   -1.311052  setosa
## 5   -1.0184372  1.24503015    -1.335752   -1.311052  setosa
## 6   -0.5353840  1.93331463    -1.165809   -1.048667  setosa

Train-test split

set.seed(101)

sample <- sample.split(final.data$Species, SplitRatio = 0.70)
train <- subset(final.data, sample = TRUE)
test <- subset(final.data, sample = FALSE)

Build a KNN model.

# Fit KNN model
k <- 10
knn_model <- knn(train[, 1:2], test[, 1:2], train[, 5], k = k)

# Combine predictions with test data
test_with_pred <- cbind(test, Predicted_Species = knn_model)

# Plotting using ggplot2 and ggpubr
ggplot(test_with_pred, aes(x = Sepal.Width, y = Sepal.Length, color = Predicted_Species)) +
  geom_point(size = 3, alpha = 0.9) +
  stat_ellipse(aes(fill = Predicted_Species, color = Predicted_Species), geom = "polygon", alpha = 0.4,color = "white") +
  labs(title = paste("KNN Cluster Probability Ellipses (k = ", k, ")", sep = ""),
       x = "Sepal.Length", y = "Sepal.Width", fill = "Predicted Species", color = "Predicted Species") +
  scale_color_manual(values = c("setosa" = "blue", "versicolor" = "green", "virginica" = "red")) +
  scale_fill_manual(values = c("setosa" = "lightblue", "versicolor" = "lightgreen", "virginica" = "lightcoral")) +
  dark_theme_light()

## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().

# Fit KNN model
k <- 5
knn_model <- knn(train[, 1:2], test[, 1:2], train[, 5], k = k)

# Combine predictions with test data
test_with_pred <- cbind(test, Predicted_Species = knn_model)

# Plotting using ggplot2 and ggpubr
knn.plt <- ggplot() +
  geom_point(data = train, aes(x = Sepal.Width, y = Sepal.Length, color = "Train"), size = 3, alpha = 0.7) +
  geom_point(data = test_with_pred, aes(x = Sepal.Width, y = Sepal.Length, color = Predicted_Species), size = 3, alpha = 0.7) +
  stat_ellipse(data = train, aes(x = Sepal.Width, y = Sepal.Length, fill = "Train"), geom = "polygon", alpha = 0.2, color = "black") +
  stat_ellipse(data = test_with_pred, aes(x = Sepal.Width, y = Sepal.Length, fill = Predicted_Species), geom = "polygon", alpha = 0.2, color = "black") +
  labs(title = paste("KNN Cluster Probability Ellipses (k =", k, ")", sep = ""),
       x = "Sepal.Length", y = "Sepal.Width", fill = "Dataset") +
  scale_color_manual(values = c("Train" = "blue", "setosa" = "green", "versicolor" = "yellow", "virginica" = "red")) +
  scale_fill_manual(values = c("Train" = "lightblue", "setosa" = "lightgreen", "versicolor" = "lightcoral", "virginica" = "lightyellow")) +
  dark_theme_light()

ggplty <- ggplotly(knn.plt) 

ggplty